Model benchmarks
bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF local LLM performance
As of October 2026, bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF runs at up to 12.4 tok/s for local inference (best of 3 community benchmark runs across 1 GPU).
Model size
8B
Peak speed
12.4 tok/s
Average speed
11.6 tok/s
Avg PP
453.3 tok/s
Min memory
n/a
Max context
65,535 tokens
Avg output / run
11,879 tokens
Avg runtime / run
22m 43s
Avg quality
12.9
Benchmark runs
3
GPUs tested
1
Quality by task
Average LLM-judged quality (0–100) with the run-to-run spread shown as a P5–P95 band, overall and for each benchmark task, across all 3 runs. The low and high columns show how much the judge’s score varies between runs, and need at least two runs to display.
| Task | P5 (low) | Avg | P95 (high) |
|---|---|---|---|
| Overall | 9.5 | 12.9 | 17.7 |
| Agent Workflow | 15.5 | 19.4 | 25.8 |
| Code Generation | 0.0 | 0.5 | 1.3 |
| Role Play & Narrative | 17.0 | 23.0 | 26.7 |
| Research & Analysis | 1.4 | 8.5 | 17.4 |
Performance by hardware and tool
Every hardware/tool/quantization combination bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF has been benchmarked on, ranked by peak token generation speed. Last updated October 2026.
| Hardware | Tool | Quant | Best tok/s | Avg tok/s | Memory | Context | Quality | Runs |
|---|---|---|---|---|---|---|---|---|
| Intel Arc B390 | llama.cpp | — | 12.4 tok/s | 11.6 tok/s | n/a | 65,535 tokens | 12.9 | 3 |
Benchmark runs
All 3 bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF runs submitted so far — expand one for its hardware, quality breakdown and per-scenario detail.
Frequently asked questions
- Is bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF good for coding?
- In our benchmarks, bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF scores 0.5/100 for coding. It runs at about 11.6 tok/s, so if you want more speed, IsValorum/Nex-N2.5-mini-APEX-I-MiniPlus-V2.1-GGUF:1 is faster (~25.7 tok/s) and still scores well for coding (85.0/100).
- Is bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF good for agentic (tool-using) tasks?
- In our benchmarks, bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF scores 19.4/100 for agentic workflows. It runs at about 11.6 tok/s, so if you want more speed, granite-4.2-8b-Q4_K_L is faster (~71.6 tok/s) and still scores well for agentic workflows (92.9/100).
- How fast is bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF for local inference?
- Across 3 community benchmark runs, bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF reaches up to 12.4 tok/s and averages 11.6 tok/s, with the fastest results on Intel Arc B390.
- Which tools have been used to run bartowski/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF?
- Benchmarks were submitted using llama.cpp. Results are community-contributed and updated as new runs arrive.